PyHRF is a set of tools for within-subject fMRI data analysis, focused on the
characterization of the hemodynamics.

Within the chain of fMRI data processing, these tools provide alternatives to
the classical within-subject GLM fitting procedure. The inputs are preprocessed
images (except spatial smoothing) and the outputs are the contrast maps and the
HRF estimates.

The package is mainly written in Python and provides the implementation of the
two following methods:

The joint-detection estimation (JDE) approach, which divides the brain
into functionally homogeneous regions and provides one HRF estimate per
region as well as response levels specific to each voxel and each
experimental condition. This method embeds a temporal regularization on the
estimated HRFs and an adaptive spatial regularization on the response levels.

The Regularized Finite Impulse Response (RFIR) approach, which provides
HRF estimates for each voxel and experimental conditions. This method embeds
a temporal regularization on the HRF shapes, but proceeds independently
across voxels (no spatial model). See Introduction for a more detailed
overview.

PyHRF is currently under the CeCILL licence version 2.
Originally developed by the former LNAO (Neurospin, CEA),
pyHRF is now entering (since Sep 2014) in a new era under the joint
collaboration of the the Parietal team (Inria Saclay)
and the MISTIS team (Inria Rhones-Alpes).